synthlab0/network-anomaly-logs-fr

🔐 Network Anomaly Logs — French Industrial Infrastructure

Description

50,000 synthetic network logs from French industrial infrastructure environments, with expert anomaly annotations.

Built by a cybersecurity practitioner with real-world experience in VLAN segmentation, pfSense, and Zabbix monitoring environments. Data is realistic, structured, and ready to use for ML training.

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📊 Statistics

Split	Examples	Normal	Anomalies
Train	40,000	34,000	6,000
Val	5,000	4,250	750
Test	5,000	4,250	750
Total	50,000	42,500	7,500

• Anomaly rate: 15%
• Format: JSONL (one example per line)

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🚨 Anomaly Types

Type	Description	Severity
port_scan	Sequential port scanning	Medium
data_exfiltration	Large outbound transfer to unknown IP	Critical
brute_force_ssh	Repeated failed SSH login attempts	High
ddos_flood	Volumetric UDP/ICMP flood	Critical
c2_communication	Periodic C2 server beaconing	Critical
lateral_movement	Internal machine-to-machine movement	High
dns_tunneling	Data exfiltration via DNS queries	High
credential_dump	Access to authentication resources	Critical

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📋 Schema

Each example contains the following fields:

{
  "id": "log_00000001",
  "timestamp": "2025-01-01T00:29:52.311Z",
  "src_ip": "10.10.30.229",
  "src_port": 64132,
  "src_vlan": "CLI",
  "dst_ip": "194.165.84.245",
  "dst_port": 443,
  "dst_vlan": "EXTERNAL",
  "protocol": "TCP",
  "bytes_sent": 1240,
  "bytes_received": 8430,
  "packets_sent": 3,
  "packets_received": 12,
  "duration_ms": 234,
  "ttl": 128,
  "tcp_flags": "ACK",
  "user_agent": null,
  "is_anomaly": false,
  "anomaly_type": null,
  "severity": null,
  "description": null,
  "label": "normal_https_browse"
}

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🏗️ Infrastructure Context

Logs simulate a segmented French industrial network with 4 VLANs:

VLAN	Subnet	Role
ADM	10.10.10.0/24	Administration
SRV	10.10.20.0/24	Servers
CLI	10.10.30.0/24	Clients
EXTERNAL	—	Internet

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🎯 Use Cases

• Network Intrusion Detection Systems (NIDS)
• Anomaly detection model training & fine-tuning
• Cybersecurity benchmark evaluation
• SIEM rule validation & testing
• ML research on network security

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📦 Access

This dataset is gated — request access using the button above.

Access is free. Once approved, you will receive a download link with:

• ✅ Full 50,000 examples (train / val / test splits)
• ✅ Python generation script
• ✅ Commercial use license (CC BY 4.0)

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📜 License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Commercial use allowed with attribution.

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📬 Contact & Custom Datasets

Need a custom dataset with specific anomaly types, volume, or format?

📧 Contact: soon mail incoming